Neural Architecture Challenge – Hard Level
A rigorous assessment of cutting‑edge concepts in neural network design and training, crafted for seasoned practitioners.
Quiz Questions
Answer all questions below and test your knowledge.
- 1
Which statement accurately reflects the universal approximation theorem for feedforward networks?
- 2
What primary mechanism causes the vanishing gradient problem in deep networks using sigmoid activations?
- 3
What is the computational complexity of self‑attention in a standard transformer relative to sequence length?
- 4
Which initialization scheme is specifically designed for deep networks with ReLU activations?
- 5
For multi‑label classification where each label is independent, which loss function is most appropriate?
- 6
How does label smoothing influence a model's confidence during training?
- 7
Which optimizer explicitly tracks both first‑order and second‑order moment estimates of gradients?
- 8
Which regularization technique directly penalizes large weight magnitudes in the loss function?
- 9
What factors primarily determine the representational capacity of a convolutional layer?
- 10
Which activation function is unbounded in both positive and negative directions?
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